[1] "Age10" "Age26" "Age5" "BDDM" "DemoQ" "DemoY107"
[7] "DemoY108" "dicRitw01" "Income"
DemoY108 - 村里季度資料
97 98 99 100 101 102 103 104 105 106 107 108
7822 7834 7835 7835 7835 7839 7851 7851 7851 7851 7760 7760
BIG6 = c("桃園市","臺北市","臺中市","臺南市","高雄市","新北市")
Y = DemoY108 %>% select(
year, vid=`村里代碼`, city=`縣市名稱`, county=`鄉鎮市區名稱`,
vname=`村里名稱`, house=`戶數`, pop=`人口數`, fm.ratio=`性比例`,
sp.ratio=`扶養比`, elderly=`老化指數`) %>%
filter(! city %in% c("連江縣","金門縣","澎湖縣")) %>%
mutate(city = case_when(
city == "臺北縣" ~ "新北市",
city == "桃園縣" ~ "桃園市",
city == "高雄縣" ~ "高雄市",
city == "臺南縣" ~ "臺南市",
city == "臺中縣" ~ "臺中市",
TRUE ~ city ))
Y %>% is.na %>% colSums year vid city county vname house pop fm.ratio
0 0 0 0 0 0 0 0
sp.ratio elderly
0 0
Q = DemoQ %>% transmute(
year = str_remove(`資料時間`,"Y.*$") %>% as.integer,
qtr = str_remove(`資料時間`,"^\\d+Y"),
vid=`村里代碼`, city=`縣市名稱`, county=`鄉鎮市區名稱`, vname=`村里名稱`,
born=`出生數`, death=`死亡數`, marriage=`結婚對數`, devorce=`離婚對數`,
time=`資料時間`
) %>%
filter(! city %in% c("連江縣","金門縣","澎湖縣")) %>%
mutate(city = case_when(
city == "臺北縣" ~ "新北市",
city == "桃園縣" ~ "桃園市",
city == "高雄縣" ~ "高雄市",
city == "臺南縣" ~ "臺南市",
city == "臺中縣" ~ "臺中市",
TRUE ~ city )) %>%
mutate_at(vars(born:devorce), ~replace_na(as.integer(.), 0))#六都中出生、死亡、結婚、離婚的比例
Q %>%
group_by (city) %>%
filter(city == c("新北市","高雄市","臺北市","臺中市","臺南市","桃園市")) %>%
summarise(birth=sum(born,na.rm = T),
death = sum(death,na.rm = T),
devorce = sum(death,na.rm = T),
marriage = sum(death,na.rm = T)) %>% ungroup %>%
mutate(city = case_when(
city == "臺北縣"~"新北市",
city == "桃園縣"~"桃園市",
city == "高雄縣"~"高雄市",
city == "臺南縣"~"臺南市",
city == "臺中縣"~"臺中市",
TRUE ~ city)) %>%
gather(key="BDDM",value="Pop",2:5) %>% #(2:5是什麼意思?????)
ggplot(aes(x=city, y=Pop, fill=BDDM)) +
geom_col(position="fill")Warning in city == c("新北市", "高雄市", "臺北市", "臺中市", "臺南市", "桃園
市"): 較長的物件長度並非較短物件長度的倍數
Warning in city == c("新北市", "高雄市", "臺北市", "臺中市", "臺南市", "桃園
市"): 較長的物件長度並非較短物件長度的倍數
Warning in city == c("新北市", "高雄市", "臺北市", "臺中市", "臺南市", "桃園
市"): 較長的物件長度並非較短物件長度的倍數
Warning in city == c("新北市", "高雄市", "臺北市", "臺中市", "臺南市", "桃園
市"): 較長的物件長度並非較短物件長度的倍數
Warning in city == c("新北市", "高雄市", "臺北市", "臺中市", "臺南市", "桃園
市"): 較長的物件長度並非較短物件長度的倍數
Warning in city == c("新北市", "高雄市", "臺北市", "臺中市", "臺南市", "桃園
市"): 較長的物件長度並非較短物件長度的倍數
Warning in city == c("新北市", "高雄市", "臺北市", "臺中市", "臺南市", "桃園
市"): 較長的物件長度並非較短物件長度的倍數
Warning in city == c("新北市", "高雄市", "臺北市", "臺中市", "臺南市", "桃園
市"): 較長的物件長度並非較短物件長度的倍數
Warning in city == c("新北市", "高雄市", "臺北市", "臺中市", "臺南市", "桃園
市"): 較長的物件長度並非較短物件長度的倍數
Warning in city == c("新北市", "高雄市", "臺北市", "臺中市", "臺南市", "桃園
市"): 較長的物件長度並非較短物件長度的倍數
Warning in city == c("新北市", "高雄市", "臺北市", "臺中市", "臺南市", "桃園
市"): 較長的物件長度並非較短物件長度的倍數
六都中出生率最高的是臺北市,而最低的是高雄市與臺南市。 六都中死亡率最高的是臺南市,反觀桃園市死亡率最低。 六都中離婚率最高的是臺南市,相較至下桃園市最低。 六都中結婚率最高的是高雄市與臺南市,其他四個城市不相上下。
#六都中扶老比及扶幼比所占的比率
#從DemoY108中主要撈出扶養比、扶幼比、扶老筆的資料
C = DemoY108 %>% select(
year, vid=`村里代碼`, city=`縣市名稱`, county=`鄉鎮市區名稱`,
vname=`村里名稱`, house=`戶數`, pop=`人口數`, rise_r=`扶養比`,
young_r=`扶幼比`, elder_r=`扶老比`) %>%
filter(! city %in% c("連江縣","金門縣","澎湖縣")) %>%
mutate(city = case_when(
city == "臺北縣" ~ "新北市",
city == "桃園縣" ~ "桃園市",
city == "高雄縣" ~ "高雄市",
city == "臺南縣" ~ "臺南市",
city == "臺中縣" ~ "臺中市",
TRUE ~ city ))
C %>% is.na %>% colSums year vid city county vname house pop rise_r young_r elder_r
0 0 0 0 0 0 0 0 0 0
#畫出六都中扶老比及扶幼比所占的比率
C %>%
filter(city == c("新北市","高雄市","臺北市","臺中市","臺南市","桃園市")) %>%
group_by(year,city) %>%
summarise(young_r = sum(young_r), elder_r = sum(elder_r)) %>%
gather(key = "category", value = "rise_r", 3:4) %>%
ggplot(aes(x = year, y = rise_r, col=category)) +
geom_point() +
facet_wrap(~city)Warning in city == c("新北市", "高雄市", "臺北市", "臺中市", "臺南市", "桃園
市"): 較長的物件長度並非較短物件長度的倍數
可看出扶老比逐漸上升–>老年化社會 扶幼比逐漸下降–>少子化社會
Income_data = Income %>% select(
year=`資料時間`, vid=`村里代碼`, city=`縣市名稱`, county=`鄉鎮市區名稱`,
vname=`村里名稱`, income=`各類所得金額合計`) %>%
filter(! city %in% c("連江縣","金門縣","澎湖縣")) %>%
mutate(city = case_when(
city == "臺北縣" ~ "新北市",
city == "桃園縣" ~ "桃園市",
city == "高雄縣" ~ "高雄市",
city == "臺南縣" ~ "臺南市",
city == "臺中縣" ~ "臺中市",
TRUE ~ city ))
Income_data$year <- substr(Income_data$year ,1, 3)
Income_data$year <- as.integer(Income_data$year)
df_income = Income_data %>% group_by(city, year) %>% summarise(
`平均所得` = sum(income)
) %>% filter(year %in% seq(99,108,3))
df_income$merge = paste(df_income$city , df_income$year) A = Income %>% select(
year="資料時間", inc="各類所得金額合計", city="縣市名稱") %>%
filter(inc !=is.na(inc)) %>%
mutate(city = case_when(
city == "臺北縣"~"新北市",
city == "桃園縣"~"桃園市",
city == "高雄縣"~"高雄市",
city == "臺南縣"~"臺南市",
city == "臺中縣"~"臺中市",TRUE ~ city)) %>%
group_by(city, year) %>%
summarise(Inc = sum(inc))
A$year <- substr(A$year ,1, 3)
A$year <- as.integer(A$year)
A %>% filter(city %in% BIG6) %>%
ggplot(aes(x=year, y=Inc, col=city)) + #pop是人口數
geom_line() +
geom_vline(xintercept=103 , col='red')依年份比較各縣市的各類所得金額合計(100Y~106Y) 所得為前五高的縣市排名:台北市>新北市>台中市>=高雄市>桃園市 其中在100年時高雄市原本略高於台中市,但在103年之後台中的所得開始超越高雄,且差距有越來越多的趨勢,可見近年來台中的經濟發展比高雄更好。
4.畫出各縣市所得和老化指數的關係
BIG6 = c("桃園市","臺北市","臺中市","臺南市","高雄市","新北市")
scale1 = function(mp=0) scale_color_gradient2(
midpoint=mp, low="seagreen4", mid="wheat2", high="firebrick2")
df_new %>% #filter(city.x %in% BIG6) %>%
ggplot(aes(x=`老化指數`, y=`平均所得`, col=`性比例`, size=`人口`, label=year.x)) +
geom_point(alpha=1) + scale1(100) + theme_bw() +
facet_wrap(~city.x, ncol=4) -> g; ggplotly(g)發現偏鄉所得普遍偏低,但人口老化程度嚴重 而在六都之中,高雄和台南市較有「又老又窮」的現象